import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


A						=			'▁'

A						=			{'vocab_file': 'spiece.model'}

A						=			{
    'vocab_file': {'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'}
}

A						=			{
    'google/pegasus-xsum': 5_1_2,
}


A						=			logging.get_logger(__name__)





class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		lowerCAmelCase_     =   VOCAB_FILES_NAMES

		lowerCAmelCase_     =   VOCAB_FILES_NAMES
		lowerCAmelCase_     =   PRETRAINED_VOCAB_FILES_MAP
		lowerCAmelCase_     =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowerCAmelCase_     =   ['input_ids', 'attention_mask']
		def __init__(       self     :     Any     ,  snake_case__     :     Optional[Any]     ,  snake_case__     :     str="<pad>"     ,  snake_case__     :     Union[str, Any]="</s>"     ,  snake_case__     :     Optional[int]="<unk>"     ,  snake_case__     :     Any="<mask_2>"     ,  snake_case__     :     Any="<mask_1>"     ,  snake_case__     :     Optional[Any]=None     ,  snake_case__     :     Tuple=1_0_3     ,  snake_case__     :     Optional[Dict[str, Any]] = None     ,  **snake_case__     :     List[str]     ,  )      ->	None:
				_lowerCamelCase				   =  offset
				if additional_special_tokens is not None:
						if not isinstance(snake_case__     ,  snake_case__		):
								raise TypeError(
								    f"""additional_special_tokens should be of type {type(snake_case__		)}, but is"""
								    f""" {type(snake_case__		)}"""		)

						_lowerCamelCase				   =  (
						    ([mask_token_sent] + additional_special_tokens)
						    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
						    else additional_special_tokens
						)
						# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
						additional_special_tokens_extended += [
						    f"""<unk_{i}>""" for i in range(len(snake_case__		)     ,  self.offset - 1		)
						]

						if len(set(snake_case__		)		) != len(snake_case__		):
								raise ValueError(
								    'Please make sure that the provided additional_special_tokens do not contain an incorrectly'
								    f""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."""		)
						_lowerCamelCase				   =  additional_special_tokens_extended
				else:
						_lowerCamelCase				   =  [mask_token_sent] if mask_token_sent is not None else []
						additional_special_tokens += [f"""<unk_{i}>""" for i in range(2     ,  self.offset		)]

				_lowerCamelCase				   =  {} if sp_model_kwargs is None else sp_model_kwargs

				super().__init__(
				    eos_token=snake_case__     ,  unk_token=snake_case__     ,  mask_token=snake_case__     ,  pad_token=snake_case__     ,  mask_token_sent=snake_case__     ,  offset=snake_case__     ,  additional_special_tokens=snake_case__     ,  sp_model_kwargs=self.sp_model_kwargs     ,  **snake_case__     ,  )
				_lowerCamelCase				   =  mask_token_sent
				_lowerCamelCase				   =  vocab_file
				_lowerCamelCase				   =  spm.SentencePieceProcessor(**self.sp_model_kwargs		)
				self.sp_model.Load(snake_case__		)

				# add special tokens to encoder dict
				_lowerCamelCase				   =  {
				    0: self.pad_token,
				    1: self.eos_token,
				}

				if self.mask_token_sent is not None:
						self.encoder.update(
						    {
						        2: self.mask_token_sent,
						        3: self.mask_token,
						    }		)

				if self.offset > 0:
						# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
						# mask_token_sent is already added to list -> so start at 1
						self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1     ,  self.offset - 1		)}		)

				_lowerCamelCase				   =  {v: k for k, v in self.encoder.items()}
		@property
		def   _snake_case       (       self     :     Optional[Any]		)      ->	int:
				return len(self.sp_model		) + self.offset
		def   _snake_case       (       self     :     int		)      ->	Dict[str, int]:
				_lowerCamelCase				   =  {self.convert_ids_to_tokens(snake_case__		): i for i in range(self.vocab_size		)}
				vocab.update(self.added_tokens_encoder		)
				return vocab
		def __getstate__(       self     :     List[Any]		)      ->	Dict:
				_lowerCamelCase				   =  self.__dict__.copy()
				_lowerCamelCase				   =  None
				return state
		def __setstate__(       self     :     str     ,  snake_case__     :     Tuple		)      ->	Optional[Any]:
				_lowerCamelCase				   =  d

				# for backward compatibility
				if not hasattr(self     ,  'sp_model_kwargs'		):
						_lowerCamelCase				   =  {}

				_lowerCamelCase				   =  spm.SentencePieceProcessor(**self.sp_model_kwargs		)
				self.sp_model.Load(self.vocab_file		)
		def   _snake_case       (       self     :     Dict     ,  snake_case__     :     str		)      ->	List[str]:
				return self.sp_model.encode(snake_case__     ,  out_type=snake_case__		)
		def   _snake_case       (       self     :     int     ,  snake_case__     :     str		)      ->	int:
				if token in self.decoder:
						return self.decoder[token]
				elif token in self.added_tokens_decoder:
						return self.added_tokens_decoder[token]
				_lowerCamelCase				   =  self.sp_model.piece_to_id(snake_case__		)
				return sp_id + self.offset
		def   _snake_case       (       self     :     int     ,  snake_case__     :     int		)      ->	str:
				if index in self.encoder:
						return self.encoder[index]
				elif index in self.added_tokens_encoder:
						return self.added_tokens_encoder[index]
				else:
						_lowerCamelCase				   =  self.sp_model.IdToPiece(index - self.offset		)
				return token
		def   _snake_case       (       self     :     int     ,  snake_case__     :     List[Any]		)      ->	str:
				_lowerCamelCase				   =  []
				_lowerCamelCase				   =  ''
				for token in tokens:
						# make sure that special tokens are not decoded using sentencepiece model
						if token in self.all_special_tokens:
								out_string += self.sp_model.decode(snake_case__		) + token
								_lowerCamelCase				   =  []
						else:
								current_sub_tokens.append(snake_case__		)
				out_string += self.sp_model.decode(snake_case__		)
				return out_string.strip()
		def   _snake_case       (       self     :     List[Any]     ,  snake_case__     :     int=False		)      ->	List[str]:
				return 1
		def   _snake_case       (       self     :     Any     ,  snake_case__     :     Union[str, Any]		)      ->	str:
				_lowerCamelCase				   =  set(self.all_special_ids		)  # call it once instead of inside list comp
				all_special_ids.remove(self.unk_token_id		)  # <unk> is only sometimes special

				return [1 if x in all_special_ids else 0 for x in seq]
		def   _snake_case       (       self     :     Optional[Any]     ,  snake_case__     :     List     ,  snake_case__     :     Optional[List] = None     ,  snake_case__     :     bool = False		)      ->	List[int]:
				if already_has_special_tokens:
						return self._special_token_mask(snake_case__		)
				elif token_ids_a is None:
						return self._special_token_mask(snake_case__		) + [1]
				else:
						return self._special_token_mask(token_ids_a + token_ids_a		) + [1]
		def   _snake_case       (       self     :     Tuple     ,  snake_case__     :     int     ,  snake_case__     :     Optional[int]=None		)      ->	List[int]:
				if token_ids_a is None:
						return token_ids_a + [self.eos_token_id]
				# We don't expect to process pairs, but leave the pair logic for API consistency
				return token_ids_a + token_ids_a + [self.eos_token_id]



		def   _snake_case       (       self     :     List[str]     ,  snake_case__     :     str     ,  snake_case__     :     Optional[str] = None		)      ->	Tuple[str]:
				if not os.path.isdir(snake_case__		):
						logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""		)
						return
				_lowerCamelCase				   =  os.path.join(
				    snake_case__     ,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']		)

				if os.path.abspath(self.vocab_file		) != os.path.abspath(snake_case__		) and os.path.isfile(self.vocab_file		):
						copyfile(self.vocab_file     ,  snake_case__		)
				elif not os.path.isfile(self.vocab_file		):
						with open(snake_case__     ,  'wb'		) as fi:
								_lowerCamelCase				   =  self.sp_model.serialized_model_proto()
								fi.write(snake_case__		)

				return (out_vocab_file,)
from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass





@dataclass
class     lowerCAmelCase__     :


		'''simple docstring'''






		lowerCAmelCase_     =   42
		lowerCAmelCase_     =   42





class     lowerCAmelCase__     :


		'''simple docstring'''






		def __init__(       self     :     int     ,  snake_case__     :     int		)      ->	Any:
				_lowerCamelCase				   =  [[] for _ in range(snake_case__		)]
				_lowerCamelCase				   =  size
		def __getitem__(       self     :     Dict     ,  snake_case__     :     int		)      ->	Iterator[Edge]:
				return iter(self._graph[vertex]		)
		@property
		def   _snake_case       (       self     :     str		)      ->	int:
				return self._size
		def   _snake_case       (       self     :     List[Any]     ,  snake_case__     :     int     ,  snake_case__     :     int     ,  snake_case__     :     int		)      ->	Tuple:
				if weight not in (0, 1):
						raise ValueError('Edge weight must be either 0 or 1.'		)

				if to_vertex < 0 or to_vertex >= self.size:
						raise ValueError('Vertex indexes must be in [0; size).'		)

				self._graph[from_vertex].append(Edge(snake_case__     ,  snake_case__		)		)



		def   _snake_case       (       self     :     Dict     ,  snake_case__     :     int     ,  snake_case__     :     int		)      ->	int | None:
				_lowerCamelCase				   =  deque([start_vertex]		)
				_lowerCamelCase				   =  [None] * self.size
				_lowerCamelCase				   =  0

				while queue:
						_lowerCamelCase				   =  queue.popleft()
						_lowerCamelCase				   =  distances[current_vertex]
						if current_distance is None:
								continue

						for edge in self[current_vertex]:
								_lowerCamelCase				   =  current_distance + edge.weight
								_lowerCamelCase				   =  distances[edge.destination_vertex]
								if (
								    isinstance(snake_case__     ,  snake_case__		)
								    and new_distance >= dest_vertex_distance
								):
										continue
								_lowerCamelCase				   =  new_distance
								if edge.weight == 0:
										queue.appendleft(edge.destination_vertex		)
								else:
										queue.append(edge.destination_vertex		)

				if distances[finish_vertex] is None:
						raise ValueError('No path from start_vertex to finish_vertex.'		)

				return distances[finish_vertex]


if __name__ == "__main__":
				import doctest

				doctest.testmod()